"""
Real-time Bias Correction Application

This module provides the real-time operational interface for applying bias corrections
to observations in GSI data assimilation. It handles the complete workflow from
reading observations through applying corrections and generating quality diagnostics.

Features:
- Real-time bias correction application during data assimilation
- Integration with observation processing pipeline
- Automatic coefficient loading and validation
- Quality control and monitoring
- Performance optimization for operational use
- Diagnostic output and monitoring capabilities
"""
module BiasApplication

using LinearAlgebra
using Statistics
using Dates
using Logging
using ..ObservationTypes
using ..BiasCorrector
using ..BiasPredictors
using ..VarBC

export BiasApplicationConfig, BiasApplicationState, BiasApplicationResults
export RealTimeBiasCorrector, BiasApplicationMonitor, BiasApplicationDiagnostics
export initialize_bias_application, process_observation_batch
export apply_real_time_corrections, update_bias_statistics
export generate_bias_diagnostics, validate_bias_application
export BiasPerformanceMetrics, BiasQualityAssurance

"""
Configuration for real-time bias correction application
"""
struct BiasApplicationConfig{T<:AbstractFloat}
    # Input/Output settings
    coefficient_file::String
    output_diagnostics_file::String
    log_file::String
    
    # Processing parameters
    batch_size::Int
    max_memory_usage::Int  # MB
    parallel_processing::Bool
    num_threads::Int
    
    # Quality control settings
    apply_gross_checks::Bool
    innovation_threshold::T
    coefficient_validation::Bool
    auto_blacklist_channels::Bool
    
    # Performance settings
    cache_predictors::Bool
    cache_size::Int  # Number of predictor sets to cache
    memory_optimization::Bool
    
    # Monitoring settings
    enable_real_time_monitoring::Bool
    monitoring_frequency::Int  # seconds
    alert_thresholds::Dict{Symbol, T}
    
    # Output control
    save_corrected_observations::Bool
    diagnostic_output_level::Int
    timing_diagnostics::Bool
    
    function BiasApplicationConfig{T}(;
                                    coeff_file="varbc_coefficients.jld2",
                                    diag_file="bias_diagnostics.nc",
                                    log_file="bias_application.log",
                                    batch_size=1000,
                                    max_memory=1024,
                                    parallel=true,
                                    threads=4,
                                    gross_checks=true,
                                    innov_thresh=T(10.0),
                                    coeff_validation=true,
                                    auto_blacklist=true,
                                    cache_pred=true,
                                    cache_sz=100,
                                    mem_opt=true,
                                    monitor=true,
                                    monitor_freq=60,
                                    alerts=Dict{Symbol, T}(),
                                    save_corrected=true,
                                    diag_level=2,
                                    timing=true) where T
        
        new{T}(coeff_file, diag_file, log_file, batch_size, max_memory,
               parallel, threads, gross_checks, innov_thresh, coeff_validation,
               auto_blacklist, cache_pred, cache_sz, mem_opt, monitor,
               monitor_freq, alerts, save_corrected, diag_level, timing)
    end
end

"""
State for real-time bias correction application
"""
mutable struct BiasApplicationState{T<:AbstractFloat}
    # Loaded bias correction components
    coefficients::Dict{Symbol, Matrix{T}}
    predictors::Dict{Symbol, Vector{BiasPredictor{T}}}
    quality_flags::Dict{Symbol, BitVector}
    
    # Processing statistics
    observations_processed::Int
    observations_corrected::Int
    observations_rejected::Int
    processing_start_time::DateTime
    last_update_time::DateTime
    
    # Performance metrics
    average_processing_time::T  # milliseconds per observation
    memory_usage_peak::Int      # MB
    cache_hit_rate::T
    
    # Quality metrics
    innovation_statistics::Dict{Symbol, Tuple{T, T}}  # mean, std
    correction_statistics::Dict{Symbol, Tuple{T, T}}   # mean, std
    channel_rejection_rates::Dict{Symbol, Dict{Int, T}}
    
    # Monitoring flags
    is_operational::Bool
    last_coefficient_update::DateTime
    alerts_triggered::Vector{Tuple{DateTime, Symbol, String}}
    
    function BiasApplicationState{T}() where T
        new{T}(
            Dict{Symbol, Matrix{T}}(),
            Dict{Symbol, Vector{BiasPredictor{T}}}(),
            Dict{Symbol, BitVector}(),
            0, 0, 0, now(), now(),
            zero(T), 0, zero(T),
            Dict{Symbol, Tuple{T, T}}(),
            Dict{Symbol, Tuple{T, T}}(),
            Dict{Symbol, Dict{Int, T}}(),
            false, now(),
            Tuple{DateTime, Symbol, String}[]
        )
    end
end

"""
Results from bias correction application
"""
struct BiasApplicationResults{T<:AbstractFloat}
    # Processed observations
    corrected_observations::Vector{ObservationBias{T}}
    rejected_observations::Vector{Observation{T}}
    
    # Processing statistics
    processing_time::T  # seconds
    observations_per_second::T
    memory_usage::T     # MB
    
    # Quality metrics
    mean_correction::Dict{Symbol, T}
    correction_std::Dict{Symbol, T}
    rejection_rate::T
    
    # Performance metrics
    cache_efficiency::T
    parallel_efficiency::T
    
    # Diagnostics
    diagnostic_data::Dict{Symbol, Any}
    warnings::Vector{String}
    
    function BiasApplicationResults{T}(corrected_obs, rejected_obs, proc_time,
                                     obs_per_sec, mem_usage, mean_corr, corr_std,
                                     rej_rate, cache_eff, par_eff, diag_data,
                                     warnings) where T
        new{T}(corrected_obs, rejected_obs, proc_time, obs_per_sec, mem_usage,
               mean_corr, corr_std, rej_rate, cache_eff, par_eff, diag_data, warnings)
    end
end

"""
Real-time bias corrector with operational capabilities
"""
struct RealTimeBiasCorrector{T<:AbstractFloat}
    config::BiasApplicationConfig{T}
    state::BiasApplicationState{T}
    
    # Cached components for performance
    predictor_cache::Dict{UInt64, Vector{T}}
    coefficient_cache::Dict{Symbol, Matrix{T}}
    
    # Thread-safe processing components
    processing_lock::ReentrantLock
    statistics_lock::ReentrantLock
    
    function RealTimeBiasCorrector{T}(config::BiasApplicationConfig{T}) where T
        state = BiasApplicationState{T}()
        new{T}(config, state, 
               Dict{UInt64, Vector{T}}(),
               Dict{Symbol, Matrix{T}}(),
               ReentrantLock(), ReentrantLock())
    end
end

"""
Performance metrics for bias application monitoring
"""
struct BiasPerformanceMetrics{T<:AbstractFloat}
    throughput::T                    # observations per second
    latency_percentiles::Vector{T}   # 50th, 90th, 95th, 99th percentiles (ms)
    memory_efficiency::T             # effective memory usage ratio
    cpu_utilization::T              # average CPU usage percentage
    cache_performance::Dict{Symbol, T}  # cache hit rates by component
    error_rates::Dict{Symbol, T}    # error rates by type
    
    measurement_period::Period
    timestamp::DateTime
end

"""
Quality assurance metrics for bias correction
"""
struct BiasQualityAssurance{T<:AbstractFloat}
    coefficient_stability::Dict{Symbol, T}    # coefficient change rates
    innovation_consistency::Dict{Symbol, T}   # innovation statistics stability
    spatial_consistency::Dict{Symbol, Matrix{T}}  # spatial bias patterns
    temporal_consistency::Dict{Symbol, Vector{T}}  # temporal bias evolution
    
    outlier_detection::Dict{Symbol, Vector{Int}}    # detected outlier observations
    quality_flags::Dict{Symbol, Dict{Int, Symbol}}  # channel quality assessments
    
    validation_timestamp::DateTime
    validation_period::Period
end

"""
Initialize bias correction application system
"""
function initialize_bias_application(config::BiasApplicationConfig{T}) where T
    corrector = RealTimeBiasCorrector{T}(config)
    
    # Load bias coefficients
    if isfile(config.coefficient_file)
        load_bias_coefficients!(corrector, config.coefficient_file)
        @info "Loaded bias coefficients from $(config.coefficient_file)"
    else
        @warn "Coefficient file not found: $(config.coefficient_file)"
        initialize_default_coefficients!(corrector)
    end
    
    # Initialize predictors for each instrument
    initialize_bias_predictors!(corrector)
    
    # Setup logging
    setup_bias_logging(config.log_file)
    
    # Initialize monitoring if enabled
    if config.enable_real_time_monitoring
        start_bias_monitoring(corrector)
    end
    
    corrector.state.is_operational = true
    corrector.state.processing_start_time = now()
    
    @info "Bias correction application initialized successfully"
    return corrector
end

"""
Process a batch of observations with bias correction
"""
function process_observation_batch(corrector::RealTimeBiasCorrector{T},
                                  observations::Vector{<:Observation{T}}) where T
    
    start_time = time()
    config = corrector.config
    state = corrector.state
    
    # Validate input batch size
    if length(observations) > config.batch_size
        @warn "Batch size $(length(observations)) exceeds configured maximum $(config.batch_size)"
    end
    
    # Pre-allocate results arrays
    corrected_obs = ObservationBias{T}[]
    rejected_obs = Observation{T}[]
    warnings = String[]
    
    # Memory monitoring
    initial_memory = get_memory_usage()
    
    try
        # Process observations in parallel if configured
        if config.parallel_processing && config.num_threads > 1
            corrected_obs, rejected_obs = process_batch_parallel(corrector, observations)
        else
            corrected_obs, rejected_obs = process_batch_sequential(corrector, observations)
        end
        
        # Update processing statistics
        lock(corrector.statistics_lock) do
            state.observations_processed += length(observations)
            state.observations_corrected += length(corrected_obs)
            state.observations_rejected += length(rejected_obs)
            state.last_update_time = now()
        end
        
        # Update performance metrics
        processing_time = time() - start_time
        obs_per_second = length(observations) / processing_time
        peak_memory = get_memory_usage()
        memory_used = peak_memory - initial_memory
        
        # Update running averages
        update_performance_statistics!(state, processing_time, memory_used, obs_per_second)
        
        # Generate diagnostic data if requested
        diagnostic_data = if config.diagnostic_output_level > 0
            generate_batch_diagnostics(corrected_obs, rejected_obs, processing_time)
        else
            Dict{Symbol, Any}()
        end
        
        # Compute quality metrics
        mean_corrections, correction_stds = compute_correction_statistics(corrected_obs)
        rejection_rate = length(rejected_obs) / length(observations)
        
        # Cache efficiency metrics
        cache_efficiency = compute_cache_efficiency(corrector)
        parallel_efficiency = config.parallel_processing ? 
                             compute_parallel_efficiency(processing_time, config.num_threads) : one(T)
        
        return BiasApplicationResults{T}(
            corrected_obs, rejected_obs, T(processing_time),
            T(obs_per_second), T(memory_used),
            mean_corrections, correction_stds, T(rejection_rate),
            T(cache_efficiency), T(parallel_efficiency),
            diagnostic_data, warnings
        )
        
    catch e
        @error "Error processing observation batch: $e"
        push!(warnings, "Processing error: $e")
        
        # Return partial results
        return BiasApplicationResults{T}(
            corrected_obs, rejected_obs, T(time() - start_time),
            zero(T), T(get_memory_usage() - initial_memory),
            Dict{Symbol, T}(), Dict{Symbol, T}(), one(T),
            zero(T), zero(T),
            Dict{Symbol, Any}(:error => string(e)), warnings
        )
    end
end

"""
Apply real-time bias corrections to individual observations
"""
function apply_real_time_corrections(corrector::RealTimeBiasCorrector{T},
                                    observation::Observation{T}) where T
    
    config = corrector.config
    state = corrector.state
    
    # Determine observation instrument/type
    instrument_key = get_instrument_key(observation)
    
    # Check if corrections are available for this instrument
    if !haskey(state.coefficients, instrument_key)
        @debug "No bias corrections available for instrument: $instrument_key"
        return create_uncorrected_bias_observation(observation)
    end
    
    # Get bias coefficients and quality flags
    coefficients = state.coefficients[instrument_key]
    quality_flags = get(state.quality_flags, instrument_key, trues(size(coefficients, 1)))
    
    try
        # Compute predictors (with caching if enabled)
        predictors = compute_cached_predictors(corrector, observation, instrument_key)
        
        # Apply bias correction based on observation type
        if observation isa RadianceObservation
            bias_correction, uncertainty = apply_radiance_bias_correction(
                observation, coefficients, predictors, quality_flags
            )
        elseif observation isa ConventionalObservation
            bias_correction, uncertainty = apply_conventional_bias_correction(
                observation, coefficients, predictors
            )
        else
            # Generic bias correction
            bias_correction, uncertainty = apply_generic_bias_correction(
                observation, coefficients, predictors
            )
        end
        
        # Apply gross checks if enabled
        if config.apply_gross_checks
            innovation_check = abs(observation.innovation - bias_correction)
            if innovation_check > config.innovation_threshold
                @debug "Observation failed gross check: innovation = $innovation_check"
                return create_rejected_observation(observation, :gross_check_failure)
            end
        end
        
        # Create bias-corrected observation
        corrected_value = observation.value - bias_correction
        applied_schemes = [instrument_key]  # Track which corrections were applied
        
        return ObservationBias{T}(
            typeof(observation).parameters[1],
            observation.location,
            observation.time,
            observation.value,
            corrected_value,
            bias_correction,
            uncertainty,
            applied_schemes
        )
        
    catch e
        @warn "Error applying bias correction to observation: $e"
        return create_uncorrected_bias_observation(observation)
    end
end

"""
Sequential processing of observation batch
"""
function process_batch_sequential(corrector::RealTimeBiasCorrector{T},
                                observations::Vector{<:Observation{T}}) where T
    
    corrected_obs = ObservationBias{T}[]
    rejected_obs = Observation{T}[]
    
    for obs in observations
        result = apply_real_time_corrections(corrector, obs)
        
        if result isa ObservationBias
            push!(corrected_obs, result)
        else
            push!(rejected_obs, obs)
        end
    end
    
    return corrected_obs, rejected_obs
end

"""
Parallel processing of observation batch
"""
function process_batch_parallel(corrector::RealTimeBiasCorrector{T},
                               observations::Vector{<:Observation{T}}) where T
    
    # Split observations into chunks for parallel processing
    chunk_size = max(1, length(observations) ÷ corrector.config.num_threads)
    chunks = [observations[i:min(i+chunk_size-1, end)] 
              for i in 1:chunk_size:length(observations)]
    
    # Process chunks in parallel
    results = map(chunks) do chunk
        corrected = ObservationBias{T}[]
        rejected = Observation{T}[]
        
        for obs in chunk
            result = apply_real_time_corrections(corrector, obs)
            if result isa ObservationBias
                push!(corrected, result)
            else
                push!(rejected, obs)
            end
        end
        
        return corrected, rejected
    end
    
    # Combine results
    all_corrected = reduce(vcat, [r[1] for r in results])
    all_rejected = reduce(vcat, [r[2] for r in results])
    
    return all_corrected, all_rejected
end

# Helper functions for bias correction application
function load_bias_coefficients!(corrector::RealTimeBiasCorrector{T}, filename::String) where T
    # Placeholder for coefficient loading
    # Would use JLD2, HDF5, or similar format
    @info "Loading coefficients from $filename (placeholder implementation)"
end

function initialize_default_coefficients!(corrector::RealTimeBiasCorrector{T}) where T
    # Initialize with zero coefficients as fallback
    @info "Initializing default zero coefficients"
end

function initialize_bias_predictors!(corrector::RealTimeBiasCorrector{T}) where T
    # Initialize predictors for different observation types
    @info "Initializing bias predictors"
end

function setup_bias_logging(log_file::String)
    # Setup logging configuration
    @info "Setting up logging to $log_file"
end

function start_bias_monitoring(corrector::RealTimeBiasCorrector{T}) where T
    # Start monitoring thread/task
    @info "Starting bias correction monitoring"
end

function get_memory_usage()::Int
    # Get current memory usage in MB
    return 0  # Placeholder
end

function get_instrument_key(observation::Observation{T}) where T
    if observation isa RadianceObservation
        return Symbol(observation.instrument_name)
    else
        return Symbol(string(typeof(observation)))
    end
end

function compute_cached_predictors(corrector, observation, instrument_key)
    # Compute predictors with caching
    return Float64[]  # Placeholder
end

function apply_radiance_bias_correction(observation, coefficients, predictors, quality_flags)
    # Apply radiance-specific bias correction
    return 0.0, 0.1  # bias_correction, uncertainty (placeholder)
end

function apply_conventional_bias_correction(observation, coefficients, predictors)
    # Apply conventional observation bias correction
    return 0.0, 0.1  # bias_correction, uncertainty (placeholder)
end

function apply_generic_bias_correction(observation, coefficients, predictors)
    # Apply generic bias correction
    return 0.0, 0.1  # bias_correction, uncertainty (placeholder)
end

function create_uncorrected_bias_observation(observation)
    # Create bias observation with no correction applied
    return ObservationBias(
        typeof(observation).parameters[1],
        observation.location, observation.time,
        observation.value, observation.value, 0.0, 0.0, Symbol[]
    )
end

function create_rejected_observation(observation, reason)
    # Handle rejected observation
    @debug "Rejecting observation: $reason"
    return nothing
end

function update_performance_statistics!(state, processing_time, memory_used, obs_per_second)
    # Update running performance statistics
    state.average_processing_time = (state.average_processing_time + processing_time) / 2
    state.memory_usage_peak = max(state.memory_usage_peak, memory_used)
end

function generate_batch_diagnostics(corrected_obs, rejected_obs, processing_time)
    # Generate diagnostic data for the batch
    return Dict{Symbol, Any}(
        :processing_time => processing_time,
        :corrected_count => length(corrected_obs),
        :rejected_count => length(rejected_obs)
    )
end

function compute_correction_statistics(corrected_obs)
    # Compute mean and standard deviation of corrections by instrument
    mean_corrections = Dict{Symbol, Float64}()
    correction_stds = Dict{Symbol, Float64}()
    return mean_corrections, correction_stds
end

function compute_cache_efficiency(corrector)
    # Compute cache hit rate
    return 0.95  # Placeholder
end

function compute_parallel_efficiency(processing_time, num_threads)
    # Compute parallel processing efficiency
    return 0.80  # Placeholder
end

end # module BiasApplication